Distributed parameter estimation of discrete hierarchical models via marginal likelihoods

Helene Massam; Nanwei Wang

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arXiv · 1310.5666

Distributed parameter estimation of discrete hierarchical models via marginal likelihoods

Helene Massam·Nanwei Wang

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Abstract

We consider discrete graphical models Markov with respect to a graph $G$ and propose two distributed marginal methods to estimate the maximum likelihood estimate of the canonical parameter of the model. Both methods are based on a relaxation of the marginal likelihood obtained by considering the density of the variables represented by a vertex $v$ of $G$ and a neighborhood. The two methods differ by the size of the neighborhood of $v$. We show that the estimates are consistent and that those obtained with the larger neighborhood have smaller asymptotic variance than the ones obtained through the smaller neighborhood.

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Helene Massam, Nanwei Wang. 2013-10-21. Distributed parameter estimation of discrete hierarchical models via marginal likelihoods. https://arxiv.org/abs/1310.5666

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